Privacy Preserving Demand Forecasting to Encourage Consumer Acceptance of Smart Energy Meters

Autor:	Briggs, Christopher, Fan, Zhong, Andras, Peter
Rok vydání:	2020
Předmět:	Computer Science - Machine Learning Computer Science - Computers and Society
Druh dokumentu:	Working Paper
Popis:	In this proposal paper we highlight the need for privacy preserving energy demand forecasting to allay a major concern consumers have about smart meter installations. High resolution smart meter data can expose many private aspects of a consumer's household such as occupancy, habits and individual appliance usage. Yet smart metering infrastructure has the potential to vastly reduce carbon emissions from the energy sector through improved operating efficiencies. We propose the application of a distributed machine learning setting known as federated learning for energy demand forecasting at various scales to make load prediction possible whilst retaining the privacy of consumers' raw energy consumption data. Comment: Accpeted at the Tackling Climate Change with Machine Learning workshop at NeurIPS 2020
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2012.07449 Zobrazit plný text záznamu View this record from Arxiv